Exploring gender preferences in French Ph.D.

Authors

Affiliations

Thomas Delcey

Université de Bourgogne

Aurelien Goutsmedt

UC Louvain; ICHEC

[1] "The path for data is /home/agoutsmedt/google_drive/Phd_Project/data"

In another blog post, we explored some features of a structural topic model to analyze our data. Specifically, we examined how topic prevalences were influenced by the year of defense. However, the stm framework also allows for the inclusion of additional variables that could serve as determinants of authors’ topic choices. In this blog article, we will ask to what extent gender influenced the topics of French theses. We will train a probabilistic topic model on french abstract abstract_fr and try to predict the resulted topics by authors’ gender. If you are not familiar with probabilistic topic models, we recommend reviewing the first blog post and the related references for background information.

Explore the data

First, let us examine the data and analyze the distribution of gender in the French dataset. The dataset includes two distinct variables, gender and gender_expanded, which provide information about the gender of individuals (e.g., authors, supervisors).

• gender variable contains raw information obtained from the idref repository.
• gender_expanded ariable is an enhanced version that imputes missing gender values using French census data.

A detailed explanation of these variables can be found in the documentation of the French database available on this website. We will use gender_expanded in this analysis. The gender_expanded variable is derived from the individual table, whereas the abstract_fr variable is obtained from the metadata table. To link these variables, we must utilize the edge table, which associates each thesis identifier (thesis_id) with corresponding entities (entity_id).

thesis_metadata <- readRDS(here(FR_cleaned_data_path, "thesis_metadata.rds"))
thesis_individual <- readRDS(here(FR_cleaned_data_path, "thesis_individual.rds")) 

# Show sample of each tables for illustration 

# Sample data from thesis_metadata
metadata_sample <- thesis_metadata %>% 
  select(thesis_id, abstract_fr, year_defence) %>% 
  mutate(abstract_fr = str_trunc(abstract_fr, 20)) %>% 
  slice_sample(n = 5)

# Sample data from thesis_individual
individual_sample <- thesis_individual %>% 
  select(entity_id, entity_name, entity_firstname, gender_expanded) %>% 
  slice_sample(n = 5)

# Convert samples to datatables
metadata_table <- datatable(metadata_sample, options = list(pageLength = 5,
                                                            searching = FALSE,
                                                            lengthChange = FALSE), caption = " Metadata")
individual_table <- datatable(individual_sample, options = list(pageLength = 5,
                                                        searching = FALSE,
                                                        lengthChange = FALSE), caption = " Individuals")

# Display tables side by side using HTML
htmltools::browsable(
  htmltools::tagList(
    htmltools::tags$div(style = "display: flex; gap: 20px;",
      htmltools::tags$div(style = "flex: 1;", metadata_table),
      htmltools::tags$div(style = "flex: 1;", individual_table)
    )
  )
)
# Join them with edge table
thesis_edge <- readRDS(here(FR_cleaned_data_path, "thesis_edge.rds"))

authors_doc_id <- thesis_edge %>%
  filter(entity_role == "author") %>% 
  select(entity_id, thesis_id) %>% 
  unique

authors_gender <- thesis_individual %>% 
  select(entity_id, entity_name, entity_firstname, gender_expanded)

# manage duplicate
duplicates <- thesis_metadata %>%
   filter(!is.na(duplicates)) %>%
   group_by(duplicates) %>% 
   # when the line has a duplicate, group and keep the older value
   slice_max(year_defence, n = 1, with_ties = FALSE)

thesis_metadata_no_duplicate <- thesis_metadata %>% 
  filter(is.na(duplicates)) %>% 
  bind_rows(duplicates)

# We can now select the relevant variables for stm
data <- thesis_metadata_no_duplicate %>% 
  # keep relevant columns
  select(thesis_id, abstract_fr, year_defence) %>% 
  # keep only documents with a title 
  filter(!is.na(abstract_fr)) %>%
  # double join to add a gender to each document
  left_join(authors_doc_id, by = "thesis_id") %>%
  left_join(authors_gender, by = "entity_id")  

Table 1: Sample of metadata and individual tables

We now evaluate the distribution of our data. To do so, we filter out entries where the abstract_fr or the authors’ gender_expanded information is unavailable. Figure 1 illustrates the distribution of theses with a French abstract over time, categorized by gender.

Gender distribution

data <- data %>%
  # filter NA abstract and gender
  filter(!is.na(gender_expanded), !is.na(year_defence))

gg <- data  %>% 
  group_by(gender_expanded, year_defence) %>%
  summarise(n = n()) %>%
  ungroup %>%
  mutate(tooltip = paste("Année:", year_defence, "<br>Nombre d'auteurs:", n, "<br>Genre:", gender_expanded)) %>%
  ggplot(aes(
    x = as.integer(year_defence),
    y = n,
    fill = gender_expanded,
    text = tooltip
  )) +
  geom_col() +
  theme_light() +
  labs(x = "", y = "Nombre d'auteurs", fill = "Genre") +
  scale_fill_brewer(palette = "Set3") +
  theme_light()

plotly::ggplotly(gg, tooltip = "text") %>%
  plotly::config(displayModeBar = FALSE)

Figure 1: Distribution of authors by gender

Data

distribution <- data  %>% 
  group_by(gender_expanded, year_defence) %>%
  summarise(n = n()) %>%
  ungroup 

distribution %>% 
  DT::datatable(
  extensions = 'Buttons',
  options = list(
    dom = 'Blfrtip',
    buttons = c('excel', 'csv'),
    pageLength = 12
  )
)

Table 2

Distribution du genre par année

The practice of including an abstract became common in the early 1980s and was formally institutionalized in 1984 with the Savary reform. While some abstracts exist for theses defended earlier, a closer examination reveals that these were added by librarians who created the entries. Therefore, it is reasonable to filter out theses defended before 1980.

To run our probabilistic topic model, we do the usual pre-processing process, we already described in this post. The primary difference in this instance is the inclusion of a few custom stopwords to address highly redundant terms frequently found in abstracts, such as chapter (chapitre) or thesis (thèse).

# first filter year before 1984

data <- data %>% 
  filter(year_defence > 1979)

# the rest of the script is a personal stm::textProcessor()

# TOKENIZATION

#' install spacy if necessary
#' `spacy_install(force = TRUE)`
#' `spacy_download_langmodel("fr_core_news_lg", force = TRUE)`

spacy_initialize("fr_core_news_lg") 

parsed <- data %>%
  # some pre-cleaning of abstract
  mutate(abstract_fr = str_to_lower(abstract_fr),
         abstract_fr = str_replace_all(abstract_fr, "œ", " ") %>% str_replace_all(., "˜", " "),
         abstract_fr = str_remove_all(abstract_fr, "$\\(?Résumé"),
         abstract_fr = str_replace_all(abstract_fr, "-", " "),
         abstract_fr = str_squish(abstract_fr)) %>% 
  pull(abstract_fr) %>%
  # identify words 
  spacyr::spacy_parse(multithread = TRUE)

id <- data %>% 
  distinct(thesis_id) %>% 
  ungroup %>% 
  mutate(doc_id = paste0("text", 1:n()))

parsed <- parsed %>% 
  left_join(id, join_by(doc_id)) %>% 
  select(-doc_id)


saveRDS(parsed, here(website_data_path, "parsed.rds"), compress = TRUE)

# FILTER TOKENS 

parsed <- readRDS(here(website_data_path, "parsed.rds"))

# prepare stop_words
stop_words <- bind_rows(
  get_stopwords(language = "fr", source = "stopwords-iso"),
  get_stopwords(language = "fr", source = "snowball"),
  # some titles have english expression
  get_stopwords(language = "en", source = "snowball")) %>%
  distinct(word) %>%
  pull(word)

# add custom stopwords 
custom_stop_words <- c("chapitre", 
                       "thèse",
                       "montrons",
                       "montre",
                       "analyse")

stop_words <- c(stop_words, custom_stop_words)

parsed_filtered <- parsed %>%
  # Count original ids
  mutate(original_count = n_distinct(thesis_id)) %>% 
  
  # Filter empty tokens and track removed ids
  filter(!pos %in% c("PUNCT", "SYM", "SPACE")) %>% 
  mutate(after_filter1 = n_distinct(thesis_id)) %>%
  { message("Doc removed after filter: ", unique(.$original_count) - unique(.$after_filter1)); . } %>% 
  
  # remove special character 
  filter(!token %in% c("-", "δ", "α", "σ", "γ", "東一")) %>% 
  mutate(after_filter2 = n_distinct(thesis_id)) %>%
  { message("Doc removed after filter: ", unique(.$after_filter2) - unique(.$after_filter1)); . } %>%
  
  # remove any digit token (including those with letters after digits such as 12eme)
  filter(!str_detect(token, "^\\d+.*$")) %>%
  mutate(after_filter3 = n_distinct(thesis_id)) %>%
  { message("Doc removed after filter: ", unique(.$after_filter3) - unique(.$after_filter2)); . } %>%
  
  # Remove pronouns and french trunc
  mutate(token = str_remove_all(token, "^[ld]'"),
         token = str_remove_all(token, "[[:punct:]]")) %>%
  # Filter single letters and stopwords
  filter(str_detect(token, "[[:letter:]]{2}")) %>%
  mutate(after_filter4 = n_distinct(thesis_id)) %>%
  { message("Doc removed after filter: ", unique(.$after_filter3) - unique(.$after_filter4)); . } %>%
  
  # filter list of stop words 
  filter(!token %in% stop_words) %>%
  mutate(after_filter5 = n_distinct(thesis_id)) %>%
  { message("Doc removed after filter: ", unique(.$after_filter5) - unique(.$after_filter4)); . } %>%
  
  # Create bigrams
  group_by(thesis_id, sentence_id) %>%
  mutate(bigram = ifelse(token_id < lead(token_id), str_c(token, lead(token), sep = "_"), NA)) %>%
  ungroup()

# SELECT BIGRAMS 

bigrams <- parsed_filtered %>%
  select(thesis_id, sentence_id, token_id, bigram) %>%
  filter(!is.na(bigram)) %>%
  mutate(window_id = 1:n()) %>%
  add_count(bigram) %>%
  filter(n > 10) %>%
  separate(bigram, c("word_1", "word_2"), sep = "_") %>%
  filter(if_all(starts_with("word"), ~ ! . %in% stop_words))

bigram_pmi_values <- bigrams %>%
  pivot_longer(cols = starts_with("word"), names_to = "rank", values_to = "word") %>%
  mutate(word = paste0(rank, "_", word)) %>%
  select(window_id, word, rank) %>%
  widyr::pairwise_pmi(word, window_id) %>%
  arrange(item1, pmi) %>%
  filter(str_detect(item1, "word_1")) %>%
  mutate(across(starts_with("item"), ~str_remove(., "word_(1|2)_"))) %>%
  rename(word_1 = item1,
         word_2 = item2,
         pmi_bigram = pmi) %>%
  group_by(word_1) %>%
  mutate(rank_pmi_bigram = 1:n())

bigrams_to_keep <- bigrams %>%
  left_join(bigram_pmi_values) %>%
  filter(pmi_bigram > 3) %>%
  mutate(bigram = paste0(word_1, "_", word_2)) %>%
  distinct(bigram) %>%
  mutate(keep_bigram = TRUE)

parsed_final <- parsed_filtered %>%
  left_join(bigrams_to_keep) %>%
  mutate(token = if_else(keep_bigram, bigram, token, missing = token),
         token = if_else(lag(keep_bigram), lag(bigram), token, missing = token),
         token_id = if_else(lag(keep_bigram), token_id - 1, token_id, missing = token_id)) %>%
  distinct(thesis_id, sentence_id, token_id, token)

term_list <- parsed_final %>% 
  rename(term = token)

saveRDS(term_list, here(website_data_path, "term_list.rds"))

# PREPARE STM INPUT 
term_list <- readRDS(here(website_data_path, "term_list.rds"))

# stm object with covariate 
metadata <- term_list %>%
  distinct(thesis_id) %>% 
  left_join(data, by = "thesis_id") %>% 
  mutate(year_defence = as.numeric(year_defence)) %>% 
  distinct(thesis_id, abstract_fr, year_defence, gender_expanded) %>%
  # filter lines with na covariates 
  filter(!is.na(abstract_fr),
         !is.na(gender_expanded), 
         !is.na(year_defence))

#transform list of terms into stm object 
corpus_in_dfm <- term_list %>%
  # remove observations deleted by the metadata filter 
  filter(thesis_id %in% metadata$thesis_id) %>% 
  add_count(term, thesis_id) %>%
  cast_dfm(thesis_id, term, n)

corpus_in_stm <- quanteda::convert(corpus_in_dfm, to = "stm",  docvars = metadata)

saveRDS(corpus_in_stm, here(website_data_path, "corpus_in_stm.rds"))

We can then run the topic model using gender_expanded and year_defence as covariates of the prevalences \(\theta_{1:K}\). In simple terms, instead of assuming that all topics are equally distributed across documents, the inclusion of covariates ensures that topic prevalence is shaped by the structure of the data reflected in these covariates. The covariates constrain the range of possible topic prevalences (\(\theta_d\)) for each document, aligning them with patterns in the metadata.

For a corpus of 11,000 documents, starting with \(K=100\) (the number of topics) provides a reasonable basis for exploration, balancing granularity (enough topics to capture diversity) and interpretability (avoiding overly fine-grained or redundant topics).

corpus_in_stm <- readRDS(here(website_data_path, "corpus_in_stm.rds"))

formula_str <- paste("~", "gender_expanded + s(year_defence)")
formula_obj <- as.formula(formula_str)

stm <-
  stm(
    documents = corpus_in_stm$documents,
    vocab = corpus_in_stm$vocab,
    prevalence = formula_obj,
    data = corpus_in_stm$meta,
    K = 100,
    init.type = "Spectral",
    verbose = TRUE,
    seed = 123
  )

saveRDS(stm, here(website_data_path, "stm.rds"))

Analysis

Figure 2 presents the top 20 topics ranked by average prevalence. For each topic, the most probable words from the topic content (based on the \(\beta_{1:K}\) distributions) are assigned. Unsurprisingly, the top topics closely align with common subfields in economics, such as development economics, monetary economics, and the economics of innovation.

stm <- readRDS(here(website_data_path, "stm.rds"))

label_topic <- labelTopics(stm, n = 7) 

top_terms_prob <- label_topic %>% .[[1]] %>% 
  as_tibble() %>% 
  reframe(topic_label_prob = pmap_chr(., ~ paste(c(...), collapse = ", "))) %>% 
  mutate(topic = row_number()) 

# tidy call gamma the prevalence matrix, stm calls it theta  
theta <- broom::tidy(stm, matrix = "gamma") %>% 
  # broom called stm theta matrix gamma 
  left_join(top_terms_prob, by = "topic") 

#### plot summary of topics ####

theta_mean <- theta %>%
  group_by(topic, topic_label_prob) %>%
  # broom called stm theta matrix gamma 
  summarise(theta = mean(gamma)) %>%
  ungroup %>% 
  mutate(topic = reorder(topic, theta)) %>%  
  slice_max(theta, n = 30)

theta_mean %>%
  ggplot() +
  geom_segment(
    aes(x = 0, xend = theta, y = topic, yend = topic
  ),
  color = "black",
  size = 0.5) +
    geom_text(
    aes(x = theta, y = topic, label = topic_label_prob),
    hjust = -.01,
    size = 3
  ) +
  scale_x_continuous(
    expand = c(0, 0),
    limits = c(0, max(theta_mean$theta)*3.04),
  ) +
  ggthemes::theme_hc() +
  theme(plot.title = element_text(size = 8)) +
  labs(
    x = "Average prevalence by document",
    y = NULL,
    caption =  "Note: words are most probable words"
  )

Figure 2: Top 30 topics by prevalence

Effect of gender

Now that we have estimated our topics and ensured their interpretability, we may ask to what extent the author’s gender predicts the topic prevalence for a given document. Given the increasing number of female authors over time, it is more likely that recent topics will exhibit a higher proportion of female authorship. Therefore, we include the year of defense in the regression to control for this increasing proportion.

\[ \theta_{d,k} = \beta_0 + \beta_1 * gender_d + \beta_2 * year_d + \epsilon_{d,k} \]

# create covariates and check level 
corpus_in_stm <- readRDS(here(website_data_path, "corpus_in_stm.rds"))

metadata <- corpus_in_stm$meta %>% 
  as_tibble %>%
  mutate(
    year_defence = as.numeric(year_defence),
    gender_expanded = as.factor(gender_expanded),
    gender_expanded = relevel(gender_expanded, ref = "male")
  )


# check level factor variable
# levels(metadata$gender_expanded)

# create regression formula 

formula_str <- paste("~", "gender_expanded + s(year_defence)")
formula_obj <- as.formula(formula_str)

estimate_effect <- estimateEffect(formula_obj,
                                  stm,
                                  metadata = metadata,
                                  documents = corpus_in_stm$documents,
                                  uncertainty = "Local",
                                  nsims = 25)

saveRDS(estimate_effect, here(website_data_path, "estimate_effect.rds"))

Plot

estimate_effect <-readRDS(here(website_data_path, "estimate_effect.rds"))

summary <- summary(estimate_effect)

summary_tibble <- summary$tables %>% 
  purrr::imap_dfr(~ {
    tibble(
      topic = .y,  # Extract topic number
      term = rownames(.x),  # Covariate names
      estimate = .x[, 1],  # Coefficients
      std_error = .x[, 2],  # Standard errors
      t_value = .x[, 3],  # Confidence interval lower bound 95
      p_value = .x[, 4],   # Confidence interval upper bound 95
    )
  })

gender_estimate <- summary_tibble %>% 
  filter(term == "gender_expandedfemale") 

gg <- gender_estimate %>%
  mutate(tooltip = paste("Estimate:", estimate)) %>%
  left_join(top_terms_prob, by = "topic") %>%
  mutate(
    topic = reorder(topic, estimate),
    effect = ifelse(estimate > 0, "Positive", "Negative"),
    effect = ifelse(p_value >= .1, "Not significant (90%)", effect)
  ) %>% 
  filter(p_value < 0.1) %>%
  ggplot(aes(
    x = topic,
    y = estimate,
    label = paste(topic, "-", topic_label_prob),
    fill = effect,
    text = tooltip
  )) +
  geom_col() +
  geom_text(size = 2.5, position = position_stack(vjust = .5)) +
  scale_fill_manual(
    name = "Effect",
    values = c(
      "Negative" = "#FFFFB3",
      "Positive" = "#8DD3C7",
      "Not significant (90%)" = "lightgrey"
    )
  ) +
  coord_flip() +
  ggthemes::theme_hc() +
  theme(plot.title = element_text(size = 15)) +
  labs(x = NULL, y = "Estimate")


plotly::ggplotly(gg, tooltip = "text") %>%
  plotly::config(displayModeBar = FALSE)

Figure 3: Estimate effect of being a female author on topic prevalence

Regression table

summary_tibble %>% 
  DT::datatable(
  extensions = 'Buttons',
  options = list(
    dom = 'Blfrtip',
    buttons = c('excel', 'csv'),
    pageLength = 11
  )
)

Table 3

corpus_in_stm <- readRDS(here(website_data_path, "corpus_in_stm.rds"))

metadata <- corpus_in_stm$meta %>%
  as_tibble %>%
  mutate(
    year_defence = as.numeric(year_defence),
    gender_expanded = as.factor(gender_expanded),
    gender_expanded = relevel(gender_expanded, ref = "male")
  )

estimate_effect_35 <- estimateEffect(
  c(35) ~ gender_expanded * s(year_defence),
  stm,
  metadata = metadata,
  uncertainty = "None",
  # nsims = 25
)

data_summary <- tidystm::extract.estimateEffect(
  estimate_effect_35,
  "year_defence",
  stm,
  method = "continuous",
  moderator = "gender_expanded",
  moderator.value = c("female", "male")
) %>%
  # add topic label
  left_join(top_terms_prob, by = "topic")

gg1 <- data_summary %>%
  filter(covariate.value > 1985) %>%
  ggplot(
    aes(
      x = covariate.value,
      y = estimate,
      ymin = ci.lower,
      ymax = ci.upper,
      color =  moderator.value,
      fill = moderator.value
    )
  ) +
  geom_ribbon(alpha = .5, show.legend = FALSE) +
  geom_line() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(
    title = paste("Words:", unique(data_summary$topic_label)),
    subtitle = "Intervalle à 95%",
    # Ensure topic_label is unique
    color = "Gender",
    x = "covariate.value",
    y = "Expected topic prevalence"
  ) +
  scale_fill_manual(values = c("male" = "#FFFFB3", "female" = "#8DD3C7")) +
  scale_color_manual(values = c("male" = "#FFFFB3", "female" = "#8DD3C7")) +
  theme_minimal() +
  theme(strip.text = element_text(size = 3))

estimate_effect_53 <- estimateEffect(
  c(53) ~ gender_expanded * s(year_defence),
  stm,
  metadata = metadata,
  uncertainty = "None",
  # nsims = 25
)

data_summary <- tidystm::extract.estimateEffect(
  estimate_effect_53,
  "year_defence",
  stm,
  method = "continuous",
  moderator = "gender_expanded",
  moderator.value = c("female", "male")
) %>%
  # add topic label
  left_join(top_terms_prob, by = "topic")

gg2 <- data_summary %>%
  filter(covariate.value > 1985) %>%
  ggplot(
    aes(
      x = covariate.value,
      y = estimate,
      ymin = ci.lower,
      ymax = ci.upper,
      color = moderator.value,
      fill = moderator.value
    )
  ) +
  geom_ribbon(alpha = .5, show.legend = FALSE) +
  geom_line() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(
    title = paste("Words:", unique(data_summary$topic_label)),
    subtitle = "Intervalle à 95%",
    # Ensure topic_label is unique
    color = "Gender",
    x = "covariate.value",
    y = "Expected topic prevalence"
  ) +
  scale_fill_manual(values = c("male" = "#FFFFB3", "female" = "#8DD3C7")) +
  scale_color_manual(values = c("male" = "#FFFFB3", "female" = "#8DD3C7")) +
  theme_minimal() +
  theme(strip.text = element_text(size = 3))

print(gg1)
print(gg2)

(a) Topic 35

(b) Topic 53

Figure 4: Régression de la prévalence avec interaction

# prepare color 

color <- gender_estimate %>%
  mutate(
    topic = reorder(topic, estimate),
    color = ifelse(estimate > 0, "#8DD3C7", "#FFFFB3"),
    color = ifelse(p_value >= .1, "grey", color)
  ) %>% 
  select(topic, color)

# load the model 
stm <- readRDS(here(website_data_path, "stm.rds"))

corr <- stm::topicCorr(stm)

# matrix to table
corr_table <- reshape2::melt(corr$cor) 


label_topic <- labelTopics(stm, n = 10)

nodes <- label_topic %>% .[[1]] %>%
  as_tibble() %>%
  reframe(topic_label_prob = pmap_chr(., ~ paste(c(...), collapse = ", "))) %>%
  mutate(topic = as.factor(row_number())) %>% 
  left_join(color) %>% 
  rename(source_id = topic)

edges <- corr_table %>% 
  dplyr::filter(Var1 != Var2) %>% 
  rename(source_id = Var1,
         target_id = Var2,
         weight = value) 

graph <- tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = FALSE)

# if you want to normalize weigth to handle negative value 

# graph_normalize <- graph %>% 
#   activate(edges) %>% 
#   mutate(weight = rescale(weight, to = c(0.01, 1)))


# fa 2
graph_layout <- vite::complete_forceatlas2(graph, first.iter = 50000, kgrav = 1)
     
saveRDS(corr_table, here(website_data_path, "corr_table.rds"))

saveRDS(graph_layout, here(website_data_path, "graph_layout.rds"))

Réseau de corrélation

graph_layout <- readRDS(here(website_data_path, "graph_layout.rds"))

gg <- ggraph(graph_layout, 
             "manual", 
             x = x, 
             y = y) +
  geom_edge_arc0(aes(
          width = weight), 
          alpha = 0.1, strength = 0.2, show.legend = FALSE) +
  scale_edge_width_continuous(range = c(0.01,0.2)) +
  geom_point(aes(x = x, y = y)) +
  geom_label_repel_interactive(aes(
    x = x,
    y = y,
    label = source_id,
    tooltip = topic_label_prob,
    data_id = source_id,
    color = color
  ),
  size = 4) +
  scale_color_identity() + 
  scale_size_continuous(range = c(0.5,3)) +
  labs(title = "Les noeuds sont les thématiques, les liens sont les coefficients de corrélation.") +
  theme_void()

girafe(ggobj = gg,
       width_svg  = 16,
       height_svg = 9)

Figure 5: Réseaux des thématiques (spacialisation par Force Atlas 2)

Données

corr_table <- readRDS(here(website_data_path, "corr_table.rds"))


corr_table %>% 
  DT::datatable(
  extensions = 'Buttons',
  options = list(
    dom = 'Blfrtip',
    buttons = c('excel', 'csv'),
    pageLength = 5
  )
)